Prediction Of Higher Heating Value Research Articles

National survey of dewatered sewage sludge composition in Japan: Trend of organic elements and prediction of higher heating value

National survey of dewatered sewage sludge composition in Japan: Trend of organic elements and prediction of higher heating value

ANFIS based prediction model for biomass heating value using proximate analysis components

This paper proposes a new biomass higher heating value (HHV) prediction model using adaptive neuro-fuzzy inference system (ANFIS) approach. The 444 data related to wide range biomass based materials are composed from the open literature. The input set for the prediction model is involved of the proximate analysis components such as fixed carbon, ash and volatile matter. Three methods called grid partition, sub-clustering and fuzzy c-means are considered in the ANFIS model building process, in order to generate fuzzy inference system (FIS) structure. For determining the best ANFIS based prediction model, a number of simulation studies are performed for each FIS method. The optimal result obtained from each method is compared with each other and the results of the models given in the related literature by prediction performance criteria. The results show that sub-clustering based ANFIS model is the best biomass HHV prediction model. Its obtained coefficient of regression (R2) and root mean square error (RMSE) are 0.8836 and 1.3006, respectively, in the testing phase. As a conclusion, it can be said that the proposed ANFIS based model is an efficient technique to obtain high accuracy biomass HHV prediction.

Genetic programming based high performing correlations for prediction of higher heating value of coals of different ranks and from diverse geographies

The higher heating value (HHV) is the most important indicator of a coal's potential energy yield. It is commonly used in the efficiency and optimal design calculations pertaining to the coal combustion and gasification processes. Since the experimental determination of coal's HHV is tedious and time-consuming, a number of proximate and/or ultimate analyses based correlations—which are mostly linear—have been proposed for its estimation. Owing to the fact that relationships between some of the constituents of the proximate/ultimate analyses and the HHV are nonlinear, the linear models make suboptimal predictions. Also, a majority of the currently available HHV models are restricted to the coals of specific ranks or particular geographical regions. Accordingly, in this study three proximate and ultimate analysis based nonlinear correlations have been developed for the prediction of HHV of coals by utilizing the computational intelligence (CI) based genetic programming (GP) formalism. Each of these correlations possesses following noteworthy characteristics: (i) the highest HHV prediction accuracy and generalization capability as compared to the existing models, (ii) wider applicability for coals of different ranks and from diverse geographies, and (iii) structurally lower complex than the other CI-based existing HHV models. It may also be noted that in this study, the GP technique has been used for the first time for developing coal-specific HHV models. Owing to the stated attractive features, the GP-based models proposed here possess a significant potential to replace the existing models for predicting the HHV of coals.

Prediction model of higher heating value of torrefied biomass based on the kinetics of biomass decomposition

This work presented the Higher Heating Value (HHV) prediction model of torrefied biomass based on the kinetics of biomass decomposition. The prediction values were compared with experimental data of torrefied cassava rhizome (Manihot esculenta species). Moreover, the model developed in this work was used to explain the effect of torrefaction temperature and residence time on the heating value of the torrefied biomass. The results show that the HHV prediction model gave an over prediction value compared to the HHV obtained by the bomb calorimeter. The average absolute error of the HHV prediction model was 7.03%. At the beginning of torrefaction, the HHV of the torrefied biomass rapidly increased when residence time increased. When a certain residence time was reached, the HHV tended to asymptote to a constant value, i.e. HHV at termination point of thermal degradation reaction (HHVc). The value of HHVc strongly depended on the torrefaction temperature. Increasing of HHVc and decreasing of residue mass (Mt) resulted in increasing of HHV of torrefied biomass. The model developed in this work gave a clear view of the change of HHV at various conditions of torrefaction temperature and time. This understanding leads to production planning with an efficient use of energy in torrefaction process.

Prediction of Higher Heating Value of Solid Biomass Fuels Using Artificial Intelligence Formalisms

The higher heating value (HHV) is an important property defining the energy content of biomass fuels. A number of proximate and/or ultimate analysis based predominantly linear correlations have been proposed for predicting the HHV of biomass fuels. A scrutiny of the relationships between the constituents of the proximate and ultimate analyses and the corresponding HHVs suggests that all relationships are not linear and thus nonlinear models may be more appropriate. Accordingly, a novel artificial intelligence (AI) formalism, namely genetic programming (GP) has been employed for the first time for developing two biomass HHV prediction models, respectively using the constituents of the proximate and ultimate analyses as the model inputs. The prediction and generalization performance of these models was compared rigorously with the corresponding multilayer perceptron (MLP) neural network based as also currently available high-performing linear and nonlinear HHV models. This comparison reveals that the HHV prediction performance of the GP and MLP models is consistently better than that of their existing linear and/or nonlinear counterparts. Specifically, the GP- and MLP-based models exhibit an excellent overall prediction accuracy and generalization performance with high (>0.95) magnitudes of the coefficient of correlation and low (<4.5 %) magnitudes of mean absolute percentage error in respect of the experimental and model-predicted HHVs. It is also found that the proximate analysis-based GP model has outperformed all the existing high-performing linear biomass HHV prediction models. In the case of ultimate analysis-based HHV models, the MLP model has exhibited best prediction accuracy and generalization performance when compared with the existing linear and nonlinear models. The AI-based models introduced in this paper due to their excellent performance have the potential to replace the existing biomass HHV prediction models.

Prediction of higher heating values of biomass from proximate and ultimate analyses

Two new empirical correlations based on proximate and ultimate analyses of biomass used for prediction of higher heating value (HHV) are presented in this paper. The correlations have been developed via stepwise linear regression method by using data of biomass samples (from the open literature) of varied origin and obtained from different geographical locations. The correlations have been validated via incorporation of additional biomass data. The correlation based on ultimate analysis (HHV=0.2949C+0.8250H) has a mean absolute error (MAE) lower than 5% and marginal mean bias error (MBE) at just 0.57% which indicate that it has good HHV predictive capability. The other correlation which is based on proximate analysis (HHV=0.1905VM+0.2521FC) is a useful companion correlation with low absolute MBE (0.67%). The HHV prediction accuracies of 12 other correlations introduced by other researchers are also compared in this study.

Prediction of higher heating values for saturated fatty acids from their physical properties

Higher heating values (HHVs) of fatty acids (C4–C18) were measured and correlated using linear least square regression analysis. Equations were developed for the estimation of the HHVs of saturated fatty acids from their molecular weight (Mw), density (DN) and carbon number (CN). These equations are HHV=0.0518 Mw+29.76, HHV=−93.4 DN+122.67 and HHV=0.7271 CN+31.419 with R2 values of 0.9895, 0.9798, and 0.9895, respectively. The correlations may be used for HHV estimation of mixtures of fatty acids developed from vegetable oils.